Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Skudai 81300, Johor, Malaysia.
School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81300, Johor, Malaysia.
Int J Environ Res Public Health. 2021 Jun 18;18(12):6566. doi: 10.3390/ijerph18126566.
This paper attempts to ascertain the impacts of population density on the spread and severity of COVID-19 in Malaysia. Besides describing the spatio-temporal contagion risk of the virus, ultimately, it seeks to test the hypothesis that higher population density results in exacerbated COVID-19 virulence in the community. The population density of 143 districts in Malaysia, as per data from Malaysia's 2010 population census, was plotted against cumulative COVID-19 cases and infection rates of COVID-19 cases, which were obtained from Malaysia's Ministry of Health official website. The data of these three variables were collected between 19 January 2020 and 31 December 2020. Based on the observations, districts that have high population densities and are highly inter-connected with neighbouring districts, whether geographically, socio-economically, or infrastructurally, tend to experience spikes in COVID-19 cases within weeks of each other. Using a parametric approach of the Pearson correlation, population density was found to have a moderately strong relationship to cumulative COVID-19 cases (-value of 0.000 and R of 0.415) and a weak relationship to COVID-19 infection rates (-value of 0.005 and R of 0.047). Consequently, we provide several non-pharmaceutical lessons, including urban planning strategies, as passive containment measures that may better support disease interventions against future contagious diseases.
本文试图确定人口密度对马来西亚 COVID-19 传播和严重程度的影响。除了描述病毒的时空传染风险外,本文最终旨在检验以下假设,即较高的人口密度会导致社区中 COVID-19 的毒力加剧。根据马来西亚 2010 年人口普查的数据,绘制了马来西亚 143 个区的人口密度,并将其与从马来西亚卫生部官方网站获得的累计 COVID-19 病例和 COVID-19 病例感染率进行对比。这三个变量的数据是在 2020 年 1 月 19 日至 2020 年 12 月 31 日之间收集的。根据观察结果,人口密度高且与相邻地区在地理、社会经济或基础设施方面高度互联的地区,往往会在几周内相继出现 COVID-19 病例激增的情况。通过使用 Pearson 相关系数的参数方法,发现人口密度与累计 COVID-19 病例之间存在中度强关系(-值为 0.000,R 值为 0.415),与 COVID-19 感染率之间存在弱关系(-值为 0.005,R 值为 0.047)。因此,我们提供了一些非药物干预措施的经验教训,包括城市规划策略,作为被动控制措施,可能更好地支持针对未来传染病的疾病干预措施。